下面为大家整理一篇优秀的essay代写范文 -- How does causality play a role in the design of computational science，文章讲述长期以来，因果关系和目的论是计算自然科学研究中最受欢迎的方法。大自然是由各种各样的有目的的人组成的（Krikorian，1949）。不仅人类，甚至动物或植物也可能具有很强的针对性。人类是追求无数目标的最有目的的生物，其中包括范围有限或范围广阔的事物，可以实现或无法实现的事物，甚至有益或有害的事物（Krikorian，1949）。为了追求自己的目标，非常需要了解系统的工作方式。通过改变设计中的某些因素，结果将以某种方式相应地改变。因此，所设计的产品或工具显然具有满足人类目的的功能。
How does causality play a role in the design of computational science
Causality and teleology are the most popular methodology in computation natural science research since long. Nature is populated by a large number of varied purposive beings (Krikorian, 1949). Not only are human beings, even animals or plants can be very purposive. While the human beings are the most purposive creatures that pursue innumerable objects which including something limited or wide in scope, something attainable or unattainable, even something beneficial or harmful (Krikorian, 1949). In order to pursue its own objects, knowing how the system is working is very necessary. By changing certain factors in design the outcome would change accordingly in a certain way. Therefore, the designed products or instruments are obviously functional made to fulfill human beings’ purposes.
Causality has a long history and enjoys advocacy by numerous philosophers. Its long history also demonstrates its validity and practicability. From Democritus down, most thinkers recognized that causality is the legitimate explanation to this phenomenon and thought that every objects in nature has causal properties, which is often controversial in relation to the teleological explanation recognized to apply teleology to every phase of nature based on successful physical science achievement (Krikorian, 1949). That is to say causality uncover the hypostases of nature. If one can control the initial condition and key factors in process, good result would recur and bad outcome would reduce. That is what human being always purses for all along.
As it is explained in Wikipedia, “Causality could also be referred to as 'cause and effect’ is the relation between one process (the cause) and another (the effect), where the first is understood to be partly responsible for the second.” (Wikipedia, 2015) In general, a process has many causes, which is said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of many other effects, which all lie in its future (wikipedia). What happens in past would lead to effects in future. It is considered to be fundamental to all natural science, especially physics (Hovorka, 2012). Design is inherently based upon causal claims or assumptions (Hovorka, 2012). In order to shape future events or better design a product, to figure out which factors or causes are most influential and how they impact the process is always attractive to natual science researchers.
So far, causality was described with several certain characteristics. First of all, it has a teleological just like the fire burns paper without any wish or purpose. Secondly, causality refers to the uniformities or constant features of a given phenomenon. Finally, causality is deterministic which means more predictable through the causal laws (Krikorian, 1949). Due to causality’s characteristics, researchers design experiments by setting different control factors, then observe different outcome results and use models and math to simulate and explain the relationships between the two. This methodology has already lead researchers to get some very beautiful formulas such as f=ma. Causality will continue work as the main methodology in next generation of nature science development.
The importance of causality in design with computational natural science
Just like David said, “Causality is the cement of the universe”(Hume, 1740). Causality helps people understand, predict and control results. All human activities that aim to predict or control the final effects or future events are based on the causality theory. People all want beneficial results happen again and reduce the harmful effects. According to causality theory, by changing and controlling the cause, one can change and control the future.
The importance of causality is quite obvious. In fact, most designs are grounded on the causal requirement and assumptions which are the part of the causality (Hovorka, 2012). Just as Argyris said, the design is ‘a specifications of actions to be taken (often in a specified sequence) to achieve the intended consequence’ (Argyris, 1996). If a designer does not follow the causality law, his or her outcome would become pure random and meaningless. In today’s research, people find a system can be more and more complicated. It may adopt different laws and multiple variables that may even affect or impact each other, which leading to the results unpredictable. According to the design with computation, causality provides an important support to the simulation and modeling based on natural science. Once one think a factor may influence the final result, one would add the factor and its applying law in a model to verify one’s idea. If a factor impact the result greatly, there must be a relationship between the two. Otherwise, if one fact just leads to little variation in result or its effect cannot be predicted by a math model, then a designer could get a conclusion that this factor is just noise or meaningless. Hypothesis is very causality, one hypothesis corresponds to a relationship between a set of rules and its effects. Designers puts up with different hypothesis for their design. For instance, a designer or researcher produce a set of design principles or a set of technological rules to control the final effect, which contain the basic part of causality in design (Hovorka, 2012). Thanks to the development of computational science, one can puts more factors in modeling and control the evolving process in more detail to get more precise results.
By taking advantages of physical laws or causal laws, it is possible to imitate the natural behavior and get the same phenomenons more than once. Once one phenomenon happens repeatedly, the physical law or causal laws can be viewed as effective and validly. Computational science makes experiments and modeling cost less. It gives a ideal environment for a laws to conduct without any noise and measuring error, and make the observation more convenient and available, which means more useful information provided. For instance, when observe the trajectories of pendulum, it is impossible to get the exact same trajectory in physical while it could come true as the simulation from the computer (Giuseppe, 2009). If the result of the computational model fits our observation, we can figure out the law of trajectories of pendulum. The computational science create an “ideal world” in Plato’s philosophy for designers to better understand how the physics works and leading them in designing process.
How does causality work in design with computational natural science
According to the pendulum mentioned as a physico-mathematical model, there is an attempt to express a possible structure of physical causality by the model in the design with computational natural science (Giuseppe, 2009). For example, Newton made a powerful proposition that acceleration is caused by a force which is proportionality coefficient of mass by considering the movements, and wrote equations including the f = ma, leading the dynamics accessible and intelligible. And then, he pointed out that the structure of causality would get the ability of deduction by his equations (Giuseppe, 2009). From then on, Physics gets close connection with Mathematics and creates interesting relationship which is the foundation of causality application on design with computation, especially natural science, making human beings organize the physical world propositions never like before. There are different opinions among the society, some of them, like Newton, believed that Metaphysics and latter are reality, while some laic ones prefer the opposite point. And then Einstein inversedthe causal relationship profoundly, thinking acceleration over a geodesic create force. These are great progresses of the relationship between Physics and Mathematics (Giuseppe, 2009).
There is no denying that the causality appears in every step of science development and promote generation of the relationship between Physics and Mathematics. By the way Physics and Mathematics, all the design of computational natural science is actually deterministic as well as more predictable for that modeling is point by point and the computer data is provided to the given system (Giuseppe, 2009). Continuing to the application of causality, imitation could be recognized as resemble to the causality which even be indistinguishable from it. And imitation is getting popularity in the last decades for the wildly applied of the computation of natural science. The successful achievement is based on the “naturally” digital data such as the physical principles and causal laws.
However, design seeks to create new knowledge through the process of design and the design systems are recognized as teleological in nature: these systems or organizations have a planned purpose with intension, and both designers and users have prospects to specific observable results or events as outcomes through the completion and use.(Hovorka, 2012)
The purpose of design lies in shaping previous causality and events to create a more desirable future (Boland, 2002). So, unpredictability is needed in design to provide the interface of physical process, while it is necessary to use empirical prediction as the basement in design.
The traditional cognition mostly thinks that causality expresses strict uniformity and determinism, teleology is more of freedom and indeterminism (Krikorian, 1949). Purpose or teleology aim involves means-and-end relation and depends on the future (Krikorian, 1949). Purposive events should not be interrupted as the hit-or-miss or random actions for that the teleological laws help designers just with the probability and the final event could not be predicted precisely. That means the designers especially in computational natural science should combine these two controversial concepts and apply them to the real design. For instance, although the event is actually precise and predictable, when apply the model generated bycomputation to the real project, the construction material has its own chaos properties and would generate more unpredictability which is the designers seeking for as new knowledge.
Conclusion
In conclusion, based on the theory of causality, this essay has examined the importance of causality in design with computational natural science for the imitation under the physical laws which could provide more useful information. And then by analyzing how does causality works with design, this essay argues the limitation of causality, it is suggested that teleology is necessary to connect with causality which makes the predictability more unpredictable, and there are several design examples to support critical opinion. Furthermore, designers or researchers should pay more attention to the combination of different means-and-end explanations instead of just focus on the simple causality theory application.
References：
Giuseppe, L. (2009). Critique of Computational Reason in the Natural Sciences. D ́epartementd’Informatique.
Hovorka, D. S. (2012). Untangling Causality in Design Science Theorising. Information Systems
Foundations: Theory building in information system, pp.69-86.
Krikorian, Y. H. (1949). Teleology and Causality. The Review of Metaphysics, Vol. 2, No. 8, pp. 35-46.
Gopnik, A. (2013). Causality. The Oxford Handbook of Developmental Psychology, Vol. 1: Body and Mind.
Argyris, C. (1996). Actionable knowledge: design causality in the service of consequential theory. The Journal of Applied Behavioral Science, 32(4), pp. 390–406.
Hume, D. (1740). An abstract of a treatise of human nature.
Boland, R. J. (2002). Design in the punctuation of management action.
Wikipedia (2015). ‘Causality’. Available from: https://en.wikipedia.org/wiki/Causality.
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How does causality play a role in the design of computational science
Causality and teleology are the most popular methodology in computation natural science research since long. Nature is populated by a large number of varied purposive beings (Krikorian, 1949). Not only are human beings, even animals or plants can be very purposive. While the human beings are the most purposive creatures that pursue innumerable objects which including something limited or wide in scope, something attainable or unattainable, even something beneficial or harmful (Krikorian, 1949). In order to pursue its own objects, knowing how the system is working is very necessary. By changing certain factors in design the outcome would change accordingly in a certain way. Therefore, the designed products or instruments are obviously functional made to fulfill human beings’ purposes.
Causality has a long history and enjoys advocacy by numerous philosophers. Its long history also demonstrates its validity and practicability. From Democritus down, most thinkers recognized that causality is the legitimate explanation to this phenomenon and thought that every objects in nature has causal properties, which is often controversial in relation to the teleological explanation recognized to apply teleology to every phase of nature based on successful physical science achievement (Krikorian, 1949). That is to say causality uncover the hypostases of nature. If one can control the initial condition and key factors in process, good result would recur and bad outcome would reduce. That is what human being always purses for all along.
As it is explained in Wikipedia, “Causality could also be referred to as 'cause and effect’ is the relation between one process (the cause) and another (the effect), where the first is understood to be partly responsible for the second.” (Wikipedia, 2015) In general, a process has many causes, which is said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of many other effects, which all lie in its future (wikipedia). What happens in past would lead to effects in future. It is considered to be fundamental to all natural science, especially physics (Hovorka, 2012). Design is inherently based upon causal claims or assumptions (Hovorka, 2012). In order to shape future events or better design a product, to figure out which factors or causes are most influential and how they impact the process is always attractive to natual science researchers.
So far, causality was described with several certain characteristics. First of all, it has a teleological just like the fire burns paper without any wish or purpose. Secondly, causality refers to the uniformities or constant features of a given phenomenon. Finally, causality is deterministic which means more predictable through the causal laws (Krikorian, 1949). Due to causality’s characteristics, researchers design experiments by setting different control factors, then observe different outcome results and use models and math to simulate and explain the relationships between the two. This methodology has already lead researchers to get some very beautiful formulas such as f=ma. Causality will continue work as the main methodology in next generation of nature science development.
The importance of causality in design with computational natural science
Just like David said, “Causality is the cement of the universe”(Hume, 1740). Causality helps people understand, predict and control results. All human activities that aim to predict or control the final effects or future events are based on the causality theory. People all want beneficial results happen again and reduce the harmful effects. According to causality theory, by changing and controlling the cause, one can change and control the future.
The importance of causality is quite obvious. In fact, most designs are grounded on the causal requirement and assumptions which are the part of the causality (Hovorka, 2012). Just as Argyris said, the design is ‘a specifications of actions to be taken (often in a specified sequence) to achieve the intended consequence’ (Argyris, 1996). If a designer does not follow the causality law, his or her outcome would become pure random and meaningless. In today’s research, people find a system can be more and more complicated. It may adopt different laws and multiple variables that may even affect or impact each other, which leading to the results unpredictable. According to the design with computation, causality provides an important support to the simulation and modeling based on natural science. Once one think a factor may influence the final result, one would add the factor and its applying law in a model to verify one’s idea. If a factor impact the result greatly, there must be a relationship between the two. Otherwise, if one fact just leads to little variation in result or its effect cannot be predicted by a math model, then a designer could get a conclusion that this factor is just noise or meaningless. Hypothesis is very causality, one hypothesis corresponds to a relationship between a set of rules and its effects. Designers puts up with different hypothesis for their design. For instance, a designer or researcher produce a set of design principles or a set of technological rules to control the final effect, which contain the basic part of causality in design (Hovorka, 2012). Thanks to the development of computational science, one can puts more factors in modeling and control the evolving process in more detail to get more precise results.
By taking advantages of physical laws or causal laws, it is possible to imitate the natural behavior and get the same phenomenons more than once. Once one phenomenon happens repeatedly, the physical law or causal laws can be viewed as effective and validly. Computational science makes experiments and modeling cost less. It gives a ideal environment for a laws to conduct without any noise and measuring error, and make the observation more convenient and available, which means more useful information provided. For instance, when observe the trajectories of pendulum, it is impossible to get the exact same trajectory in physical while it could come true as the simulation from the computer (Giuseppe, 2009). If the result of the computational model fits our observation, we can figure out the law of trajectories of pendulum. The computational science create an “ideal world” in Plato’s philosophy for designers to better understand how the physics works and leading them in designing process.
How does causality work in design with computational natural science
According to the pendulum mentioned as a physico-mathematical model, there is an attempt to express a possible structure of physical causality by the model in the design with computational natural science (Giuseppe, 2009). For example, Newton made a powerful proposition that acceleration is caused by a force which is proportionality coefficient of mass by considering the movements, and wrote equations including the f = ma, leading the dynamics accessible and intelligible. And then, he pointed out that the structure of causality would get the ability of deduction by his equations (Giuseppe, 2009). From then on, Physics gets close connection with Mathematics and creates interesting relationship which is the foundation of causality application on design with computation, especially natural science, making human beings organize the physical world propositions never like before. There are different opinions among the society, some of them, like Newton, believed that Metaphysics and latter are reality, while some laic ones prefer the opposite point. And then Einstein inversedthe causal relationship profoundly, thinking acceleration over a geodesic create force. These are great progresses of the relationship between Physics and Mathematics (Giuseppe, 2009).
There is no denying that the causality appears in every step of science development and promote generation of the relationship between Physics and Mathematics. By the way Physics and Mathematics, all the design of computational natural science is actually deterministic as well as more predictable for that modeling is point by point and the computer data is provided to the given system (Giuseppe, 2009). Continuing to the application of causality, imitation could be recognized as resemble to the causality which even be indistinguishable from it. And imitation is getting popularity in the last decades for the wildly applied of the computation of natural science. The successful achievement is based on the “naturally” digital data such as the physical principles and causal laws.
However, design seeks to create new knowledge through the process of design and the design systems are recognized as teleological in nature: these systems or organizations have a planned purpose with intension, and both designers and users have prospects to specific observable results or events as outcomes through the completion and use.(Hovorka, 2012)
The purpose of design lies in shaping previous causality and events to create a more desirable future (Boland, 2002). So, unpredictability is needed in design to provide the interface of physical process, while it is necessary to use empirical prediction as the basement in design.
The traditional cognition mostly thinks that causality expresses strict uniformity and determinism, teleology is more of freedom and indeterminism (Krikorian, 1949). Purpose or teleology aim involves means-and-end relation and depends on the future (Krikorian, 1949). Purposive events should not be interrupted as the hit-or-miss or random actions for that the teleological laws help designers just with the probability and the final event could not be predicted precisely. That means the designers especially in computational natural science should combine these two controversial concepts and apply them to the real design. For instance, although the event is actually precise and predictable, when apply the model generated bycomputation to the real project, the construction material has its own chaos properties and would generate more unpredictability which is the designers seeking for as new knowledge.
Conclusion
In conclusion, based on the theory of causality, this essay has examined the importance of causality in design with computational natural science for the imitation under the physical laws which could provide more useful information. And then by analyzing how does causality works with design, this essay argues the limitation of causality, it is suggested that teleology is necessary to connect with causality which makes the predictability more unpredictable, and there are several design examples to support critical opinion. Furthermore, designers or researchers should pay more attention to the combination of different means-and-end explanations instead of just focus on the simple causality theory application.
References：
Giuseppe, L. (2009). Critique of Computational Reason in the Natural Sciences. D ́epartementd’Informatique.
Hovorka, D. S. (2012). Untangling Causality in Design Science Theorising. Information Systems
Foundations: Theory building in information system, pp.69-86.
Krikorian, Y. H. (1949). Teleology and Causality. The Review of Metaphysics, Vol. 2, No. 8, pp. 35-46.
Gopnik, A. (2013). Causality. The Oxford Handbook of Developmental Psychology, Vol. 1: Body and Mind.
Argyris, C. (1996). Actionable knowledge: design causality in the service of consequential theory. The Journal of Applied Behavioral Science, 32(4), pp. 390–406.
Hume, D. (1740). An abstract of a treatise of human nature.
Boland, R. J. (2002). Design in the punctuation of management action.
Wikipedia (2015). ‘Causality’. Available from: https://en.wikipedia.org/wiki/Causality.
51due留学教育原创版权郑重声明：原创优秀代写范文源自编辑创作，未经官方许可，网站谢绝转载。对于侵权行为，未经同意的情况下，51Due有权追究法律责任。主要业务有essay代写、assignment代写、paper代写、作业代写服务。
51due为留学生提供最好的作业代写服务，亲们可以进入主页了解和获取更多代写范文提供作业代写服务，详情可以咨询我们的客服QQ：800020041。
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